RapidMiner AI Hub replacing RapidMiner Server + RapidMiner Studio 9.9 Release (Updated 28 April, 2021)
RapidMiner has just released AI Hub, which is more of an evolution of the RapidMiner Server product line, than just a re-branding.
Server was originally developed to scale and offload computationally intensive tasks to servers, on-premise or in the cloud, and for data science teams to store processes and collaborate. Now it’s evolved into so much more with Go, Notebooks and Grafana.
See below for all the new features. For pricing – RapidMiner AI Hub
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New dashboard provides insight into executed and failed jobs, disk usage of projects, configured schedules, web services, and more.
New project-based repository for AI collaboration and governance. Diverse teams can work together on use cases in a central location across automated, visual and code-based authoring styles.
Projects includes fine-grained version control based on git standards. AI development lifecycles demand projects to be version-controlled, iterative, collaborative and governed.
Git-based version control tracks all changes as “snapshots” allowing users to easily “roll back” which enables smoother collaboration and conflict resolution.
Enterprise-grade identity & access management: To support collaboration at scale, a new identity and access management framework is introduced, including Single Sign-On across the platform (Go, Studio and AI Hub, JupyterHub, Grafana and our platform admin tools)
RapidMiner Notebooks, the JupyterLab interface in the RapidMiner platform, is now fully integrated with the new projects framework. Allowing seamless and easy collaboration of coders with other users and full traceability of the code-based work within the AI lifecycle.
Check and manage your system from anywhere. Designed for admins, the new app grants access to check jobs, schedules and other activities and react as needed.
– Download AI Hub app from Apple App store
– Download AI Hub app from Google Play Store.
RapidMiner Studio 9.9 new features:
Improvements to the platform’s Python integration allow users to embed their code into visual workflows that non-coders can leverage – facilitating deeper collaboration between teams + new deep learning capabilities, enhanced Python integration, and significantly faster process execution. RapidMiner 9.9 enables you to tackle complex use cases and deliver business value faster than ever before.
– Further embrace Python: Code machine learning models and data transformation steps in Python, then easily share them as operators with the non-coders on your team so they can benefit from your work. Additionally, reuse centrally governed data connections by accessing connection properties through a new API in our Python library.
– Deliver value faster: Use visual transfer learning to accelerate time-to-value for advanced use cases. Reuse past models to jumpstart new projects, or start with well-known deep learning models for image classification, facial recognition etc. Additionally, run processes up to 60x faster with our new data processing engine that is optimized for blazingly fast in-memory computation of analytics workloads.
– Overcome barriers at the edge: Easily collaborate on edge-based use cases, jointly develop and deploy projects and integrate Python code or models as needed. Use the new ‘continuous mode’ of real-time scoring nodes to integrate model inference into streaming or edge computing use cases and connect them to event streaming platforms like Apache Kafka
RapidMiner Studio 9.8 new features:
– Large files in projects: optimize performance when working with large files and effortlessly exchange content between projects
– K-means clustering using H20: Fast & Efficient
– Enhancements Deep Learning and Image mining:
– Improved windowing for time series
– Improved proxy support: simplified proxy setup with automatic configuration, improved Radoop proxy to streamline connectivity to Hadoop cluster, support for connections to utilize external data in real-time use cases
– Continued investments into data science innovations and governance: fully centralized Python and R coding environment management (now including kernels inside Notebooks), fast and efficient K-Means clustering based on H2O’s implementation and more
– Additional enhancements and bug fixes
RapidMiner Studio 9.7 new features:
– RapidMiner Studio, AI Hub and JupyterHub, now support the concept of projects, enabling you to structure and isolate your work, allowing users to collaborate while maintaining a consistent state across the entire project.
– Projects are versioned, providing features like:
– Linear backup, you can always revert to a past state (nothing is lost, no matter what you do)
– Each snapshot (project version) is fully consistent, so it’s easy to answer compliance questions like “which process trained this model”
– Traceability: snapshots log who did what, when and why (through user-written comments)
– There’s a Git server used as the version control backend. This also enables storing files of arbitrary types like .py or .csv, making your projects whole
– Direct git access for everyone working via Git, e.g. Python coders. This allows seamless, two-way integration for projects between Studio users and coders.
– Local repositories, created with RapidMiner Studio 9.7 or later can also take advantage of supporting all files on your computer (.py, .jpeg, .pdf, etc).
RapidMiner ExampleSets are now written to disk in a new file format – HDF5: This format ensures stability and performance when storing large amounts of data. Also, Python and RapidMiner Studio can exchange data easier and faster than ever before.
Improvements to time series functionality:
– New operator to Integrate time series with different methods (cumulative sum / left and right riemann sum / trapezoidal rule)
– Added the option to specify negative lags and a default lag for a set of attributes (selected by an attribute subset selector) to the Lag operator
– Unfortunately due to parameter key incompatibilities, the old version of the ‘Lag’ operator had to be deprecated and new version with the same name, but different operator key is added
– Added options to use padding for Fast Fourier Transformation and calculate the frequency of the amplitude value
Improvements to augmented machine learning:
– Auto Model reduces memory usage and run times and allows multiple Auto Model jobs to be submitted to the AI Hub at once
– Model Ops offers flexible model storage options for deployed models. Unused and ID columns are now kept in the results after scoring for enhanced audits.
Updated H2O library:
– Increases stability and performance for Gradient Boosted Trees, Logistic Regression, Deep Learning and Generalized Linear Model operators
– Gradient Boosted Trees now support monotonicity constraints. Deep Learning now exposes model weights on a separate output port. Model training can be fine-tuned using expert parameters. All parameters provided by H2O are supported.
Feel free to download a 30 day trial of RapidMiner Studio and experience enterprise AI software. After the trial, Studio is limited to 10k rows, 1 logical processor and no AutoML.